Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (11): 3123-3126.DOI: 10.11772/j.issn.1001-9081.2016.11.3123

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Optimization of extreme learning machine parameters by adaptive chaotic particle swarm optimization algorithm

CHEN Xiaoqing, LU Huijuan, ZHENG Wenbin, YAN Ke   

  1. College of Information Engineering, China Jiliang University, Hangzhou Zhejiang 310018, China
  • Received:2016-04-20 Revised:2016-07-14 Online:2016-11-10 Published:2016-11-12
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61272315), the Natural Science Foundation of Zhejiang Province (LY14F020041), the National Security Bureau Project (zhejiang-00062014AQ).

自适应混沌粒子群算法对极限学习机参数的优化

陈晓青, 陆慧娟, 郑文斌, 严珂   

  1. 中国计量大学 信息工程学院, 杭州 310018
  • 通讯作者: 陆慧娟
  • 作者简介:陈晓青(1990-),女,江苏宿迁人,硕士研究生,CCF会员,主要研究方向:机器学习、数据挖掘;陆慧娟(1962-),女,浙江东阳人,教授,博士,CCF杰出会员,主要研究方向:机器学习、模式识别、生物信息学;郑文斌(1973-),男,四川彭州人,副教授,博士,CCF高级会员,主要研究方向:机器学习、模式识别;严珂(1983-),男,新加坡人,讲师,博士,主要研究方向:机器学习、图像处理。
  • 基金资助:
    国家自然科学基金资助项目(61272315);浙江省自然科学基金资助项目(LY14F020041);国家安全总局项目(zhejiang-00062014AQ)。

Abstract: Since it was not ideal for Extreme Learning Machine (ELM) to deal with non-linear data, and the parameter randomization of ELM was not conducive for generalizing the model, an improved version of ELM algorithm was proposed. The parameters of ELM were optimized by Adaptive Chaotic Particle Swarm Optimization (ACPSO) algorithm to increase the stability of the algorithm and improve the accuracy of ELM for gene expression data classification. The simulation experiments were carried out on the UCI gene data. The results show that Adaptive Chaotic Particle Swarm Optimization-Extreme Learning Machine (ACPSO-ELM) has good stability and reliability, and effectively improves the accuracy of gene classification over existing algorithms, such as Detecting Particle Swarm Optimization-Extreme Learning Machine (DPSO-ELM) and Particle Swarm Optimization-Extreme Learning Machine (PSO-ELM).

Key words: adaptive, Extreme Learning Machine (ELM), chaotic particle swarm, gene classification

摘要: 针对极限学习机(ELM)在处理非线性数据时效果不理想,并且ELM的参数随机化不利于模型泛化的特点,提出了一种改进的极限学习机算法。结合自适应混沌粒子群(ACPSO)算法对ELM的参数进行优化,以增强算法的稳定性,提高ELM对基因表达数据分类的精度。在UCI基因数据集上进行仿真实验,实验结果表明,与探测粒子群-极限学习机(DPSO-ELM)、粒子群-极限学习机(PSO-ELM)等算法相比,自适应混沌粒子群-极限学习机(ACPSO-ELM)算法具有较好的稳定性、可靠性,且能有效提高基因分类精度。

关键词: 自适应, 极限学习机, 混沌粒子群, 基因分类

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